| 管道漏磁检测信号的支持向量机识别方法 |
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| 引用本文： | 杨海英.管道漏磁检测信号的支持向量机识别方法[J].科学技术与工程,2010,10(27). |
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| 作者姓名： | 杨海英 |
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| 作者单位： | 1. 大庆石油学院,机械科学与工程学院,大庆,163453;大庆石油学院,大庆油田采油工程研究院,大庆,163453
2. 大庆石油学院,机械科学与工程学院,大庆,163453 |
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| 摘 要： | 通过对漏磁检测信号的分析,引入支持向量机,给出了特征量、训练集的选择方法以及核函数和支持向量机的分类算法,将漏磁检测信号的根据信号的定性识别和定量分析有机结合起来.可以在漏磁检测样本有限的情况下,获得最优识别结果.
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| 关 键 词： | 管道漏磁检测 信号识别 支持向量机 分类算法 |
| 收稿时间： | 6/28/2010 4:37:11 PM |
| 修稿时间： | 7/6/2010 3:13:59 PM |
Support vector machine based identification of pipeline magnetic flux leakage testing signal |
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| Yang Hai Ying.Support vector machine based identification of pipeline magnetic flux leakage testing signal[J].Science Technology and Engineering,2010,10(27). |
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| Authors: | Yang Hai Ying |
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| Institution: | YANG Hai-ying1,2,DAI Guang1,LI Wei1,SHANG Fu-hua1(Mechanical Science and Engineering Institute,Daqing Petroleum Institute1,Daqing 163318,P.R.China,Research Institute of Oil Production Engineering,Daqing Oilfield2,Daqing 163453,P.R.China) |
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| Abstract: | Based on the signal feature analysis, support vector machine is applied , and how to choose the characteristic parameters , training data , kernel function and algorithm , which combine qualitative identification and quantitative analysis effectively . On the condition of limited sample data , the best identification result can be obtained . |
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| Keywords: | pipeline magnetic flux leakage testing signal identification support vector machine classify algorithm |
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